Low-carbon optimization strategy for energy hub based on reward-punishment ladder carbon price mechanism

Abstract

In order to reduce carbon emissions and the impact of source-load uncertainty on system operation, a multi-timescale low-carbon optimization scheduling strategy in day-ahead, intra-day and real-time operations for energy hub (EH) based on a reward-punishment ladder carbon price mechanism and distributed model predictive control (DMPC) is proposed. A reward-punishment ladder carbon price calculation method is introduced and a day-ahead low-carbon optimization scheduling model for EH is constructed. A feedback closed-loop optimization strategy based on DMPC for intra-day rolling and real-time adjustments is formulated. The optimization strategy reduces source-load prediction errors and improves the efficiency of traditional model predictive control (MPC) solving. In the intra-day stage, a rolling optimization model with the objective of minimizing the sum of the ladder carbon price cost, operational cost, and penalty cost for energy storage adjustment is constructed. In the real-time stage, the overall optimization problem is decomposed, and a multi-agent real-time adjustment model based on DMPC is established. The simulation results indicate that the proposed strategy is effective in enhancing the economic efficiency of the system, reducing the uncertainty of source and load, and achieving the low-carbon, economic, stable, and reliable operation for EH.

Keywords

• energy hub (eh)
• carbon trading
• multi-timescale
• low-carbon economic dispatch
• distributed model predictive control (dmpc)
• integrated energy system (ies)
• multi-objective optimization